egnite Health

Cath Lab Capacity Is Growing: Which Interventions Are Prioritized? 🏥📈 Our national dataset reveals a shift in cath lab priorities: 🫀 AF ablations and LAACs are growing significantly 🫀 PCIs and CIEDs are declining 🫀 Structural heart procedures are growing modestly Read more by clicking the link below! #CathLabCapacity #CardiovascularCare #StructuralHeart

Claims data weren’t designed to find the patients you need. egnite’s data was. In the latest Echoes of Evidence, Hunter Nance shares how our large language models (LLMs) are trained to interpret unstructured clinical notes, capturing nuance and surfacing patient populations that structured data simply misses. If your research depends on millions of patient data, we can help. Read the latest edition of our newsletter here: https://lnkd.in/eT2Arw2s → Send us a DM if you're curious how our data can accelerate your research initiatives. #CardiovascularHealth #HealthTech #DigitalHealth

Relying on CMS data for launch planning or TAM sizing? You're likely working off insights that are 1-2 years old, sparse, and diagnostically shallow. egnite delivers near real-time, context-rich insights derived from comprehensive clinical data, powered by proprietary LLMs trained to decode unstructured clinical text. Whether you're building your GTM strategy, preparing for FDA approval, or positioning for acquisition, our data is built to move at the speed of market. Explore how our data can help inform your next business decision: https://lnkd.in/eVHmqD4T

Go-to-market strategy is only as strong as the data behind it. CMS data lags by 1–2 years and lacks the clinical nuance needed to guide strategic decisions. From post-approval forecasting to investor-facing TAM sizing, egnite delivers near real-time insights—powered by proprietary LLMs that decode unstructured clinical text—turning complex clinical data into strategic clarity. In our latest newsletter, see how our models are built, & how they help drive smarter decisions for commercial teams. https://lnkd.in/egR-vieP Curious how our data inform your next business decision? Send us a DM!

egnite’s large language models (LLMs) are purpose-built to turn unstructured clinical notes into research-ready data. In the latest Echoes of Evidence, Hunter Nance shares how these models are helping identify patient populations others miss, such as those with cardiac amyloidosis, often invisible in ICD-10 codes or claims data alone. Read the latest edition of our newsletter here: https://lnkd.in/eyu4z_4W Ready to explore how our data can help accelerate your research? Send us a DM! #CardiovascularHealth #HealthTech #DigitalHealth

Four-class GDMT prescribing in systolic heart failure has more than doubled since 2023, from 9% to 21%. However, gaps persist: 79% of systolic HF patients are not on four-class GDMT, and the average health system has roughly 1,050 systolic HF patients on no GDMT. Our 2026 Benchmark Report draws on de-identified data from more than 15.7 million patients across 47 U.S. health systems to show where therapy adoption is moving, how widely it varies by site, and how three-year survival is associated with the number of GDMT classes prescribed. Download the full report: https://lnkd.in/g5Xs7jPT #RealWorldEvidence #HeartFailure #HFrEF #GDMT